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  library_name: transformers
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- tags: []
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  library_name: transformers
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+ license: cc-by-nc-nd-4.0
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+ pipeline_tag: text-generation
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  ---
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+ # AISAK
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+ ### Overview:
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+ AISAK, short for Artificially Intelligent Swiss Army Knife, is a state-of-the-art language model designed for text generation tasks. Developed by Mandela Logan, this Language Model (LLM) is fine-tuned on extensive datasets to excel in understanding and interpreting your various queries in natural language text.
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+ ### Model Information:
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+ - **Model Name**: AISAK
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+ - **Version**: 1.0
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+ - **Model Architecture**: Mixture of Experts (MoE)
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+ - **Specialization**: The model is divided into distinct expert modules, each adept at capturing specific patterns and features within the input data.
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+ - **Gating Mechanism**: A dynamic gating mechanism intelligently selects and combines the outputs of these experts based on the input data, enhancing adaptability and performance.
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+ ### Intended Use:
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+ AISAK, conceptualized by Mandela Logan, is intricately crafted for diverse text generation applications. This sophisticated language model excels in seamlessly crafting coherent and contextually relevant textual content across an array of domains. Whether you're delving into creative writing, formulating responses, automating content creation, or simply engaging in conversation, AISAK's adaptability guarantees a smooth and versatile text generation experience. With a nuanced understanding of diverse contexts, AISAK stands as a robust tool for producing high-quality and contextually appropriate textual content across a broad spectrum of applications.
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+ ### Performance:
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+ AISAK undergoes rigorous testing across diverse input data types, consistently demonstrating superior performance. Its capabilities have proven to outperform and exceed those of various state-of-the-art models such as but not limited to, GPT-3.5 and Llama-70b.
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+ ### Ethical Considerations:
 
 
 
 
 
 
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+ - **Bias Mitigation**: Endeavors have been undertaken to address bias during training; however, users are urged to stay mindful of potential biases in the model's generated content.
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+ - **Fair Use**: Users are advised to exercise caution when incorporating AISAK in sensitive contexts and strive to ensure fair and ethical use of the generated text.
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+ ### Limitations:
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+ - While AISAK demonstrates proficiency in text generation, it might not be the most suitable option for tasks that necessitate domain-specific knowledge.
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+ - The model's performance could exhibit variations when confronted with highly specialized or out-of-domain textual data.
 
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+ ### Caveats:
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+ - It is recommended for users to double-check important decisions relying on AISAK's predictions, particularly in high-stakes scenarios.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Model Card Information:
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+ - **Model Card Created**: February 1, 2024
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+ - **Last Updated**: February 2, 2024
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+ - **Contact Information**: Please contact mandelakorilogan@gmail.com for any purpose of communication.